An Alternative Method for Predicting the Risk of Postoperative Complications in Lung Resection

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However, this technique is not available in all centers performing lung surgery, and it cannot be applied in all patients.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> The reasons are physical or medical contraindications, or structural problems in hospitals where it is implemented.Among other options, performance at symptom-limited stair-climbing test has shown the best correlation with surgical morbidity,<a class="elsevierStyleCrossRef" href="#bib0030">6</a> although its poor standardization and the difficulty for performing the test in a safe environment for the patient prevent routine use.A previous study seeking possible alternatives<a class="elsevierStyleCrossRef" href="#bib0035">7</a> found a correlation between VO2max values measured by standard cardiopulmonary exercise test (CPET) and VO2max values estimated with a regression model that included measurement of average distance (km) the patient walked each day, collected with a pedometer during the preoperative waiting period, and carbon monoxide diffusing capacity (%DLCO) adjusted for the patient's hemoglobin levels.The objectives of the present study were to improve the previously designed linear regression model, in order to find the best correlation between oxygen consumption values (measured VO2 and estimated VO2), and to compare the ability of these two variables to predict the occurrence of cardiac complications in a series of patients undergoing anatomic pulmonary resection for lung cancer.MethodsWe conducted a prospective, observational study of repeated measures for each subject before and after lung resection.Study PopulationNinety-five patients were initially included in the study, of which 12 were excluded due to inability to perform exercise testing because of joint problems that prevented smooth pedaling (n=7), vascular comorbidity (presence of aorta aneurysm and recent treatment for deep venous thrombosis; n=2), a vasovagal episode prior to the exercise test (n=1) or because of technical issues with the pedometer that prevented correct measurement of ambulatory activity (n=2).The final sample included 83 consecutive patients with non-small-cell lung cancer scheduled for major anatomic lung resection (typical segmentectomy, lobectomy, bilobectomy or pneumonectomy) from May 2009 to December 2011, who agreed to be included in the study and signed informed consent. All patients who, due to limitation or contraindication, were not able to properly perform the CPET were excluded. Inclusion of patients with ischemic heart disease was conditional on the existence of a previous coronary revascularization or evidence of good functional capacity, demonstrated by exercise testing or equivalent examination.The criteria for operability in this study have been previously published<a class="elsevierStyleCrossRef" href="#bib0040">8</a>: absence of a comorbidity of worse prognosis than cancer, Karnofski index >50%, estimated postoperative forced expiratory volume in one second (FEV1) greater than 30%, and estimated postoperative DLCO greater than 40%.All patients were treated by the same multidisciplinary team and operated via axillary thoracotomy without muscle section, in which a 2-cm auxiliary incision was also performed.Measurement of Maximum Oxygen ConsumptionCardiopulmonary exercise testing was carried out in the Respiratory Functional Test laboratory using the cardiopulmonary exercise test module (ergospirometry) MasterScreen CPX Jaeger-Vyasis-Healthcare, which enables the measurement of ventilatory parameters, VO2, carbon dioxide production (VCO2), anaerobic threshold, respiratory exchange ratio (RER), heart rate (HR), ventilatory equivalent for oxygen (EQO2), and ventilatory equivalent for CO2 (EQCO2) in an open “breath-by-breath” type system.The CPET modality used in our center is the symptom-limited incremental exercise test. In this modality, the patient performs exercise on a bicycle ergometer with continuous load increases over a period of about 10min (according to Wasserman protocol).<a class="elsevierStyleCrossRef" href="#bib0045">9</a>Measurement of Daily Physical Activity by PedometerAn OmronWalking Style Pro HJ-720IT-E2 pedometer was used with the Bi-Link downloadable software specific to this supplier.In the first visit, the pedometer the patient would use until the day of surgery was programmed with the date, time and footstep length. Participating in this study did not involve lengthening the preoperative waiting time.For analysis, the activity recorded by pedometers was reviewed on a case-by-case and day-to-day basis, excluding from the calculation of parameter means those days without any registered activity because the patient did not use the pedometer.Postoperative ComplicationsData on the type of surgery, occurrence of postoperative complications and type of complications were collected during hospital stay. Complications were grouped into those of cardiac, respiratory or technical nature.Study VariablesAll variables were prospectively collected in a computerized database:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005">-Patient variables: age, gender, body mass index (BMI), forced vital capacity (FVC), %FEV1 and %DLCO (with optimized bronchodilator treatment, if necessary); comorbidities: systemic hypertension (HT), ischemic cardiovascular disease, diabetes mellitus (DM), chronic renal failure (creatinine >2mg/dl), cardiac valve disease, and/or chronic arterial disease.</li><li class="elsevierStyleListItem" id="lsti0010">-CPET variables: patient–weight adjusted VO2peak (ml/kg/min) in absolute values and percentage.</li><li class="elsevierStyleListItem" id="lsti0015">-Pedometer variables:<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0020">•Total number of steps per day or “total steps”.</li><li class="elsevierStyleListItem" id="lsti0025">•Number of aerobic steps per day or “aerobic steps”. This count activates when the wearer has walked over 10min at a minimum rate of 60 steps per minute and inactivates if the wearer rests for more than 1min.</li><li class="elsevierStyleListItem" id="lsti0030">•Ambulatory activity time in minutes per the day or “time”, divided into total activity and aerobic activity.</li><li class="elsevierStyleListItem" id="lsti0035">•Distance traveled during daily activity in kilometers or “distance”.</li></ul></li><li class="elsevierStyleListItem" id="lsti0040">-Postoperative complications:<ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0045">•Respiratory: lobar/pulmonary atelectasis, pneumonia according to Center for Disease Control/National Healthcare Safety network criteria,<a class="elsevierStyleCrossRef" href="#bib0050">10</a> persistence of respiratory failure at discharge (PO2<60mmHg or PCO2>45mmHg), need for mechanical ventilation at any time after extubation in the operating room, and occurrence of pulmonary thromboembolism.</li><li class="elsevierStyleListItem" id="lsti0050">•Cardiovascular: occurrence of stroke, arrhythmia (auricular fibrillation, AF), myocardial ischemia or acute myocardial infarction, and heart failure.</li><li class="elsevierStyleListItem" id="lsti0055">•Technical: need for reoperation for massive hemothorax, occurrence of prolonged air leak or bronchial fistula within the first week after resection.</li></ul></li></ul>Statistical MethodDescriptive and comparative statistical analysis of the study variables was performed.After a correlation analysis between variables, a linear regression model was designed, where VO2max was the dependent variable, and the variables distance traveled, %DLCO and patient age were set as independent variables. The outcome variable in this model was estimated VO2. Bootstrap technique with 1000 replications was performed to study collinearity and perform resampling, in order to obtain a more robust standard error of the coefficients values. Finally, occurrence of postoperative morbidity and mortality and their relationship with VO2max values, both the measured values and the values estimated with the predictive model developed, were analyzed using paired Student's t-test. These analyses were performed using the statistical software Stata 10.0.ResultsFinal Study PopulationThe final study population was 83 patients, of whom 15 were women (18.1%). Population age was 64.59±9.5 (38–80 years). Twenty patients (24.1%) were being treated for hypertension, 18 (21.7%) met criteria for COPD and were receiving bronchodilators, nine (10.8%) had DM, six (7.2%) significant peripheral artery disease, five (6%) stable ischemic heart disease, and one patient (1.2%) had renal failure not needing dialysis but strict medication adjustments. None of the patients had associated valvular pathology.Patients wore the pedometer for an average of 25 days (range 5–41 days) (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>), showing a routine in the schedules and time periods they usually walked. All patients performed the CPET according to protocol.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>Seventeen typical segmentectomies, 61 lobectomies, three pneumonectomies and two bilobectomies (one right and one left) were performed.There was no mortality in this series. In terms of morbidity, 26 patients (31.3%) experienced a complication: eight (9.6%) were respiratory, one (1.2%) was a cardiac complication (arrhythmia), two patients had concomitant cardiac and respiratory complications, and 15 patients experienced technical complications (18.1%), mainly prolonged air leak.Preliminary Design Correlations and Linear Regression ModelWe analyzed the possible correlations between the recorded lung function variables and aerobic activity with VO2max and VO2peak data.In this series, VO2max was significantly associated with age (r=−0.451, P=.000), BMI (r=−0.357, P=.001), DLCO (r=0.238, P=.030), number of total steps/day (r=0.278, P=.011), and distance traveled/day (r=0.315, P=.004), but not with %FEV1 (r=0.182, P=.1), presence of COPD (r=0.088, P=.431), aerobic steps/day (r=0.151, P=.173) or the total time of aerobic activity (r=0.117, P=.294).%VO2 was significantly correlated only with %FEV1 (r=0.303, P=.006), DLCO (r=0.390, P=.000), and the number of total steps/day (r=0.244 steps, P=.027).After analyzing the correlations between variables, the final linear regression model with VO2max and %VO2 as dependent variables included those variables with the highest correlation with the dependent variable and showing no collinearity between them.Of the models developed for %VO2 and for VO2max, the best fitting was related to the estimated VO2max values (dependent variable) and included as independent variables age, DLCO and distance traveled (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). Discriminant analysis for collinearity was performed and none of the variables were related to the others (collinearity index <15 in all variables).<elsevierMultimedia ident="tbl0010"></elsevierMultimedia>Population Analysis According to Occurrence of Postoperative Cardiac ComplicationsThe two groups into which the population was divided were homogeneous (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>).<elsevierMultimedia ident="tbl0015"></elsevierMultimedia>Both variables (measured and estimated VO2) were normally distributed (K–Smirnov P>.32).As for prediction of complications, mean estimated VO2 according to the model was significantly different between patients with/without complication (P=.037), compared to measured VO2 values, which did not distinguish these groups (P=.42) (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>).<elsevierMultimedia ident="tbl0020"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia>DiscussionThe main objective of this study was to design a method for predicting the patient's VO2 from variables easily obtained in all patients, and to analyze its predictive capacity for the occurrence of cardiac complications after lung resection.The model developed had R2=0.94, meaning a very good fit. The three variables included are age, %DLCO and average distance traveled per day. The inverse relationship between age and VO2 is well known,<a class="elsevierStyleCrossRef" href="#bib0055">11</a> and inclusion of this variable into the analysis improves the result. Both %DLCO adjusted for the patient's hemoglobin and the average distance traveled per day in kilometers were already part of the previously published model.<a class="elsevierStyleCrossRef" href="#bib0035">7</a> Inclusion of these two variables represented an innovation in the variables that had been used to estimate the VO2 in other studies.<a class="elsevierStyleCrossRefs" href="#bib0060">12–14</a> In this analysis, as in the previous one,<a class="elsevierStyleCrossRef" href="#bib0035">7</a> we chose to use the variable “distance” instead of “total steps” because its correlation index with VO2 was better (r=0.315 vs r=0.278). There are two reasons for including %DLCO: first, this parameter proved to be correlated with age (r=0.245) and VO2max (r=0.238) and, second, because %DLCO is an accepted independent predictor of postoperative complications.<a class="elsevierStyleCrossRef" href="#bib0075">15</a>The second objective was to analyze the predictive ability of these two variables of oxygen consumption: the measured one and the estimated one. In our analysis, predictive ability is significantly better in the estimated model. Analyzing the performance of average values in the different groups, two aspects draw attention (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>): first, the difference between groups with/without complication is very small in both measurements; second, measured VO2 has very similar values, and non-discriminating between the two groups because the ranges of measurements are very wide, unlike estimated VO2 values whose ranges are much narrower and probably confer a predictive advantage. A recent meta-analysis of Benzo et al.<a class="elsevierStyleCrossRef" href="#bib0080">16</a> including 14 studies with 955 subjects found that patients with postoperative complications had significantly lower oxygen consumption than those who did not. However, average difference was only 3ml/kg/min or 8.95% between the two groups. The authors concluded from this analysis that patients with consumption over 20ml/kg/min had a very low complication rate. This did not mean that complications would not occur, only that in case of occurrence, they had a better chance of survival. Interestingly, the range of values of measured VO2 in patients who experienced complications in our study was higher than in those who did not (17.7–19.8 vs 16.2–21.4) and mean values were similar (18.75 vs 18.85; P=.42). However, the ranges of estimated consumption values are less wide (18.3–19.4 vs 16.1–18.5) and means were significantly different (18.87–17.35; P=.037). In the meta-analysis,<a class="elsevierStyleCrossRef" href="#bib0080">16</a> the lower threshold associated with a high complication rate is 16ml/kg/min, and this is 17.35ml/kg/min in our patients, slightly higher but within the range of values published in studies analyzed.Although the comparison of the characteristics of these two subpopulations of patients—those who experience complications and those who do not—yields two statistically similar groups, patients who experience more complications have older mean age (63.8–69.2 years) and lower %DLCO (78.2–66.7). These data are consistent with published results regarding the characteristics of patients who experience complications in other series.<a class="elsevierStyleCrossRef" href="#bib0080">16</a>Current clinical guidelines<a class="elsevierStyleCrossRef" href="#bib0005">1</a> for the evaluation of patients eligible for lung resection recommend a final decision based on the CPET results, i.e., according to the VO2max reached expressed as absolute values or percentage (%VO2).The values on which VO2max depends include age,<a class="elsevierStyleCrossRef" href="#bib0055">11</a> gender,<a class="elsevierStyleCrossRef" href="#bib0085">17</a> previous respiratory disease,<a class="elsevierStyleCrossRef" href="#bib0090">18</a> genetic characteristics,<a class="elsevierStyleCrossRef" href="#bib0095">19</a> and the training degree of the patient,<a class="elsevierStyleCrossRef" href="#bib0085">17</a> among other factors. The training degree is in turn is strongly linked to daily physical activity,<a class="elsevierStyleCrossRef" href="#bib0100">20</a> his/her health status, and mortality,<a class="elsevierStyleCrossRefs" href="#bib0105">21,22</a> and may be useful for predicting postoperative adverse events. In this regard, our group has sought an alternative to estimate VO2max value by studying performance at an easy exercise.Recent studies<a class="elsevierStyleCrossRef" href="#bib0025">5</a> confirm the existence of numerous shortcomings in the implementation of the European functional assessment algorithm published in 2009.<a class="elsevierStyleCrossRef" href="#bib0005">1</a> The major difficulty is conducting high-technology stress testing, since only 84% of operated patients underwent this for various reasons. The same guidelines recognize that the standardized symptom-limited stair climbing test can be an alternative to performing the CPET, helping reduce the number of patients who would require the latter. However, this test has been criticized for its poor standardization and difficulty for performance in a safe place for the patient. In addition, many patients have joint and insecurity problems when climbing stairs, so results can sometimes be suboptimal, as occurs with the CPET, in which some unsatisfactory results may be attributable to the harsh conditions required for its performance.Patient limitations are one of the root causes for not being able to perform the CPET.<a class="elsevierStyleCrossRef" href="#bib0035">7</a> Cardiovascular contraindications and joint limitations are especially relevant in an increasingly older population, with more associated comorbidity. Overcoming this limitation is difficult, but possibly the solution lies in the development of new simple and safe strategies to measure the exercise capacity of a given patient. This is the case of measurements based on walked distance or the quality of daily exercise that the patient performs in everyday life.It is not the intention of this study to question the predictive value of the CPET in surgical risk evaluation. At present, the measurement of daily physical activity is not considered a substitute for the formal CPET, but further studies with larger series and patients more functionally compromised might allow may support this as a valid, cost-saving and effective screening test for those patients in which a formal CPET cannot be conducted due to contraindication or limitation, or also as an alternative when CPET is not available in the center.Our study has some important limitations. On the one hand, the limitation derived from its sample size, which limits the possibilities for statistical study. This reason prevented a division of the sample into two groups: one to create the mathematical model and a second group to assess validity. Analysis of larger series in the future will enable testing of the reliability of the conclusions drawn in this study. This aspect also influences the number of cardiac complications included in the study, experienced by only 11 patients, which also limits the ability to extrapolate the results. Another important limitation of this study is a possible observation bias. The data collected were generated by the patient during everyday life outside the hospital. Therefore, it is not possible to be absolutely certain that this person was wearing the pedometer. However, in order to reduce this bias, special emphasis was made to each patient during consultation on the importance of not letting anyone else from his/her environment use the pedometer. Lastly, the findings may be affected by case-selection bias, since it was not possible to obtain data from patients who were not considered candidates for surgery, thereby not eligible for this study, due to the selection criteria of the physicians who refer patients to our unit.In conclusion, the VO2 value of a given patient can be reliably estimated from age, %DLCO and average walking distance per day, measured with a pedometer. In this series, estimated VO2max was more predictive of the occurrence of cardiac complications than VO2max measured in a standard exercise test. Our next objective will be the validation of this model in a larger series and with a broader patient profile.Conflicts of InterestThe authors declare no conflict of interest that might cause a bias in this study." "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:2 [ "identificador" => "xres319557" "titulo" => array:5 [ 0 => "Abstract" 1 => "Objectives" 2 => "Method" 3 => "Results" 4 => "Conclusion" ] ] 1 => array:2 [ "identificador" => "xpalclavsec302347" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xres319558" "titulo" => array:5 [ 0 => "Resumen" 1 => "Objetivos" 2 => "Método" 3 => "Resultados" 4 => "Conclusiones" ] ] 3 => array:2 [ "identificador" => "xpalclavsec302346" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods" "secciones" => array:6 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study Population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Measurement of Maximum Oxygen Consumption" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Measurement of Daily Physical Activity by Pedometer" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Postoperative Complications" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Study Variables" ] 5 => array:2 [ "identificador" => "sec0040" "titulo" => "Statistical Method" ] ] ] 6 => array:3 [ "identificador" => "sec0045" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0050" "titulo" => "Final Study Population" ] 1 => array:2 [ "identificador" => "sec0055" "titulo" => "Preliminary Design Correlations and Linear Regression Model" ] 2 => array:2 [ "identificador" => "sec0060" "titulo" => "Population Analysis According to Occurrence of Postoperative Cardiac Complications" ] ] ] 7 => array:2 [ "identificador" => "sec0065" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0070" "titulo" => "Conflicts of Interest" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2013-07-09" "fechaAceptado" => "2013-09-12" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec302347" "palabras" => array:6 [ 0 => "Pedometer" 1 => "Oxygen consumption" 2 => "VO2" 3 => "Surgical risk" 4 => "Lung resection" 5 => "Postoperative complications" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec302346" "palabras" => array:6 [ 0 => "Podómetro" 1 => "Prueba de esfuerzo" 2 => "VO2max" 3 => "Riesgo quirúrgico" 4 => "Resección pulmonar" 5 => "Complicaciones postoperatorias" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "ObjectivesThe aims of this study were to design a best fit linear regression model to estimate VO2max (estimated VO2) and to compare the ability of VO2 values (measured and estimated) predicting cardiorespiratory complications in a series of patients undergoing lung resection for lung cancer. MethodThis was a prospective, observational study performed in 83 patients. Variables analyzed were: demographic characteristics, comorbidity, body mass index (BMI), FEV1%, FVC%, diffusion capacity (DLCO%), mean daily distance walked in kilometers, VO2max measured by cardio-pulmonary exercise test (CPET) and postoperative complications. Descriptive and comparative statistical analysis of the variables was performed using the Mann–Whitney test for categorical variables and the Student's t-test for continuous variables. A new linear regression model was designed, where the dependent variable (measured VO2max) was estimated by the distance, DLCO% and age, resulting in the estimated VO2. The predictive power of the measured and estimated consumption was analyzed using the Student's t-test, grouping by the occurrence or absence of cardiorespiratory complications. ResultsBoth groups were homogeneous for age, sex, BMI, FEV1%, DLCO%, comorbidity, type of resection performed and mean distance walked per day. Estimated VO2 and measured VO2 were normally distributed (K–Smirnov test, P>.32). VO2 means estimated by the model (age, DLCO% and mean distance walked per day) were significantly different between patients with and without complications (Student's t-test, P=.037) compared with measured VO2 values, which did not differentiate groups (Student's t-test, P=.42). ConclusionThe VO2max estimated by the model is more predictive in this case series than the VO2max measured during a standard exercise test." ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "ObjetivosLos objetivos de este trabajo son rediseñar un modelo de regresión lineal para estimar el valor de VO2max (VO2 estimado) ya publicado y comparar la capacidad predictiva de los valores VO2 medido y VO2 estimado en la aparición de complicaciones cardiorrespiratorias en una serie de pacientes sometidos a resección pulmonar por cáncer de pulmón. MétodoEstudio prospectivo y observacional en 83 pacientes. Variables analizadas: demográficas, comorbilidad, IMC, FEV1%, FVC%, DLCO%, distancia recorrida media diaria (km), VO2max medido en el test de ejercicio cardiopulmonar (CPET) y complicación postoperatoria. Estadística descriptiva y comparativa de variables usando Mann-Whitney test para categóricas y t de Student para continuas normales. Se diseña un modelo de regresión lineal donde VO2max, la variable dependiente, se estima desde la distancia recorrida, DLCO% y edad del paciente y su resultado es la VO2 estimada. Se compara la capacidad predictiva de los VO2max medido y estimado mediante t de Student, agrupando por ocurrencia o no de complicaciones cardiorrespiratorias. ResultadosLos 2 grupos son homogéneos en edad, distribución de sexos, IMC, FEV1%, DLCO%, comorbilidad, cirugía realizada y distancia media recorrida/día. VO2 medida y VO2 estimada tienen distribución normal (K-Smirnov p>0,32). En la predicción de complicaciones, las medias del VO2 estimado a partir del modelo son significativamente diferentes entre los pacientes con/sin complicación (t de Student p=0,037); frente a los valores de VO2 medido que no distinguen grupos (t de Student p=0,42). ConclusionesEl VO2max estimado por el modelo es más predictivo en esta serie de casos que el VO2max medido en una CPET." ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Vargas Fajardo MdC, Novoa Valentín NM, Jiménez López MF, Ramos Gonzalez J, Varela Simó G. Un método alternativo para la predicción del riesgo de complicaciones postoperatorias en la resección pulmonar. 2014;50:87–92." ] ] "multimedia" => array:5 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1881 "Ancho" => 1585 "Tamanyo" => 87704 ] ] "descripcion" => array:1 [ "en" => "Differences between VO2max and VO2 mean values, estimated according to the occurrence of postoperative cardiac complications." ] ] 1 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Variable \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Minimum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Maximum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Standard Deviation \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pedometer activity data</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Total steps \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">821.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20471.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9508.83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4665.32 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Aerobic steps \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10904.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3688.47 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2989.35 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Time (min) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">108.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35.66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27.78 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Distance (km) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.37 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12.87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.19 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Measured VO2</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VO2 (ml/kg/min) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.32 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VO2 (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">116 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">78.54 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15.39 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab466432.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Characteristics of Pedometer-recorded Activity and CPET-measured Oxygen Consumption by Study Population." ] ] 2 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "R2=0.940; P<.001." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Wald test \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Significance \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">β \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">95% CI of B \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.49 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.000 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.398 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.066–0.170 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">DLCO \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.007 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.414 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.054–0.148 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Distance \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.19 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.186 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.204–0.286 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab466430.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Characteristics of the Best Fit Linear Regression Model." ] ] 3 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:2 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Variable \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Patients Without CR Complications (n=72)n (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Patients With CR Complications (n=11)n (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">P-Value \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gender: male \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57 (79) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (100) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.201 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hypertension \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (25) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (18.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">COPD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (18.05) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (45.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.055 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">DM \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (11.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (9.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IHD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (6.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Arterial disease \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (5.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (18.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.178 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">RF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab466433.png" ] ] 1 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Mean (DE) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Mean (DE) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">P-Value \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63.8 (9.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">69.2 (8.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.08 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IMC \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.7 (4.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23.9 (4.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.78 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">%FEV1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">85.1 (20.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">77.5 (24.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.26 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">%FVC \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96.4 (19.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">100.2 (19.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.53 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">%DLCO \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">78.2 (20.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">66.78 (20.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.08 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Total footsteps \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9511.2 (4701.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9493.2 (4641.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.99 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Aerobic footsteps \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3750.6 (2921.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3281.5 (3533.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.63 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Time \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">36.1 (26.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">32.6 (34.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.69 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Distance \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.9 (3.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.9 (3.03) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.349 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab466431.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Comparison of Occurrence of Postoperative Cardiac Complications (CR) in the Two Study Subpopulations." ] ] 4 => array:7 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Variable \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">n \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Range \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">P-value \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="6" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Measured VO2</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Without CR complication \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.78 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17.7–19.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.42 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">With CR complication \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16.2–21.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="6" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="6" align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Estimated VO2</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Without CR complication \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.28 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.3–19.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.037 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">With CR complication \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17.35 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16.1–18.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab466434.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Numerical Values of Mean Oxygen Consumption by Groups According to Occurrence of Cardiopulmonary Complications (CR)." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:22 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "European Respiratory Society and European Society of Thoracic Surgeons joint task force on fitness for radical therapy. 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Varela" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1183/09031936.00184308" "Revista" => array:6 [ "tituloSerie" => "Eur Respir J" "fecha" => "2009" "volumen" => "34" "paginaInicial" => "17" "paginaFinal" => "41" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/19567600" "web" => "Medline" ] ] ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0010" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The use of scoring systems in selecting patients for lung cancer resection: work-up bias come full-circle" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "M. Utley" 1 => "T. Treasure" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.thorsurg.2007.10.005" "Revista" => array:6 [ "tituloSerie" => "Thorac Surg Clin" "fecha" => "2008" "volumen" => "18" "paginaInicial" => "107" "paginaFinal" => "113" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/18402206" "web" => "Medline" ] ] ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0015" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The evolving role of exercise testing prior to lung resection" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "G.N. Olsen" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Chest" "fecha" => "1989" "volumen" => "95" "paginaInicial" => "218" "paginaFinal" => "225" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/2642409" "web" => "Medline" ] ] ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0020" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Performance at symptom-limited stair-climbing test is associated with increased cardiopulmonary complications, mortality, and costs after major lung resection" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "A. Brunelli" 1 => "M. Refai" 2 => "F. Xiumé" 3 => "M. Salati" 4 => "V. Sciarra" 5 => "L. 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An Alternative Method for Predicting the Risk of Postoperative Complications in Lung Resection

Un método alternativo para la predicción del riesgo de complicaciones postoperatorias en la resección pulmonar

Maria del Carmen Vargas Fajardoa, Nuria Maria Novoa Valentínb,

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nuria.novoa@usal.es

Corresponding author.

, Marcelo Fernando Jiménez Lópezb, Jacinto Ramos Gonzalezc, Gonzalo Varela Simób

a Servicio de Anestesiología, Hospital Universitario de Salamanca, Salamanca, Spain

b Servicio de Cirugía Torácica, Hospital Universitario de Salamanca, IBSAL, Salamanca, Spain

c Servicio de Neumología, Unidad de Pruebas Funcionales Respiratorias, Hospital Universitario de Salamanca, Salamanca, Spain

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          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Differences between VO<span class="elsevierStyleInf">2max</span> and VO<span class="elsevierStyleInf">2</span> mean values&#44; estimated according to the occurrence of postoperative cardiac complications&#46;</p>"
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Despite the refinement and adaptation of published algorithms and mathematical decision models&#44;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> prediction of surgical risk is difficult due to selection bias inherent to these models&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> Comprehensive assessment of the cardiorespiratory system using standardized exercise tests provides a lot of information about the physiological status of the patient who is to undergo pulmonary resection&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> Calculation of peak exercise oxygen consumption &#40;VO<span class="elsevierStyleInf">2max</span>&#41;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> is considered the most reliable test to quantify individual risk for postoperative complications&#46; However&#44; this technique is not available in all centers performing lung surgery&#44; and it cannot be applied in all patients&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> The reasons are physical or medical contraindications&#44; or structural problems in hospitals where it is implemented&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">Among other options&#44; performance at symptom-limited stair-climbing test has shown the best correlation with surgical morbidity&#44;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> although its poor standardization and the difficulty for performing the test in a safe environment for the patient prevent routine use&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">A previous study seeking possible alternatives<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> found a correlation between VO<span class="elsevierStyleInf">2max</span> values measured by standard cardiopulmonary exercise test &#40;CPET&#41; and VO<span class="elsevierStyleInf">2max</span> values estimated with a regression model that included measurement of average distance &#40;km&#41; the patient walked each day&#44; collected with a pedometer during the preoperative waiting period&#44; and carbon monoxide diffusing capacity &#40;&#37;DLCO&#41; adjusted for the patient&#39;s hemoglobin levels&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The objectives of the present study were to improve the previously designed linear regression model&#44; in order to find the best correlation between oxygen consumption values &#40;measured VO<span class="elsevierStyleInf">2</span> and estimated VO<span class="elsevierStyleInf">2</span>&#41;&#44; and to compare the ability of these two variables to predict the occurrence of cardiac complications in a series of patients undergoing anatomic pulmonary resection for lung cancer&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Methods</span><p id="par0025" class="elsevierStylePara elsevierViewall">We conducted a prospective&#44; observational study of repeated measures for each subject before and after lung resection&#46;</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Study Population</span><p id="par0030" class="elsevierStylePara elsevierViewall">Ninety-five patients were initially included in the study&#44; of which 12 were excluded due to inability to perform exercise testing because of joint problems that prevented smooth pedaling &#40;<span class="elsevierStyleItalic">n</span>&#61;7&#41;&#44; vascular comorbidity &#40;presence of aorta aneurysm and recent treatment for deep venous thrombosis&#59; <span class="elsevierStyleItalic">n</span>&#61;2&#41;&#44; a vasovagal episode prior to the exercise test &#40;<span class="elsevierStyleItalic">n</span>&#61;1&#41; or because of technical issues with the pedometer that prevented correct measurement of ambulatory activity &#40;<span class="elsevierStyleItalic">n</span>&#61;2&#41;&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">The final sample included 83 consecutive patients with non-small-cell lung cancer scheduled for major anatomic lung resection &#40;typical segmentectomy&#44; lobectomy&#44; bilobectomy or pneumonectomy&#41; from May 2009 to December 2011&#44; who agreed to be included in the study and signed informed consent&#46; All patients who&#44; due to limitation or contraindication&#44; were not able to properly perform the CPET were excluded&#46; Inclusion of patients with ischemic heart disease was conditional on the existence of a previous coronary revascularization or evidence of good functional capacity&#44; demonstrated by exercise testing or equivalent examination&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">The criteria for operability in this study have been previously published<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a>&#58; absence of a comorbidity of worse prognosis than cancer&#44; Karnofski index &#62;50&#37;&#44; estimated postoperative forced expiratory volume in one second &#40;FEV<span class="elsevierStyleInf">1</span>&#41; greater than 30&#37;&#44; and estimated postoperative DLCO greater than 40&#37;&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">All patients were treated by the same multidisciplinary team and operated via axillary thoracotomy without muscle section&#44; in which a 2-cm auxiliary incision was also performed&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Measurement of Maximum Oxygen Consumption</span><p id="par0050" class="elsevierStylePara elsevierViewall">Cardiopulmonary exercise testing was carried out in the Respiratory Functional Test laboratory using the cardiopulmonary exercise test module &#40;ergospirometry&#41; MasterScreen CPX Jaeger-Vyasis-Healthcare&#44; which enables the measurement of ventilatory parameters&#44; VO<span class="elsevierStyleInf">2</span>&#44; carbon dioxide production &#40;VCO<span class="elsevierStyleInf">2</span>&#41;&#44; anaerobic threshold&#44; respiratory exchange ratio &#40;RER&#41;&#44; heart rate &#40;HR&#41;&#44; ventilatory equivalent for oxygen &#40;EQO<span class="elsevierStyleInf">2</span>&#41;&#44; and ventilatory equivalent for CO<span class="elsevierStyleInf">2</span> &#40;EQCO<span class="elsevierStyleInf">2</span>&#41; in an open &#8220;breath-by-breath&#8221; type system&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">The CPET modality used in our center is the symptom-limited incremental exercise test&#46; In this modality&#44; the patient performs exercise on a bicycle ergometer with continuous load increases over a period of about 10<span class="elsevierStyleHsp" style=""></span>min &#40;according to Wasserman protocol&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Measurement of Daily Physical Activity by Pedometer</span><p id="par0060" class="elsevierStylePara elsevierViewall">An OmronWalking Style Pro HJ-720IT-E2 pedometer was used with the Bi-Link downloadable software specific to this supplier&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">In the first visit&#44; the pedometer the patient would use until the day of surgery was programmed with the date&#44; time and footstep length&#46; Participating in this study did not involve lengthening the preoperative waiting time&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">For analysis&#44; the activity recorded by pedometers was reviewed on a case-by-case and day-to-day basis&#44; excluding from the calculation of parameter means those days without any registered activity because the patient did not use the pedometer&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Postoperative Complications</span><p id="par0075" class="elsevierStylePara elsevierViewall">Data on the type of surgery&#44; occurrence of postoperative complications and type of complications were collected during hospital stay&#46; Complications were grouped into those of cardiac&#44; respiratory or technical nature&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Study Variables</span><p id="par0080" class="elsevierStylePara elsevierViewall">All variables were prospectively collected in a computerized database&#58;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">-</span><p id="par0085" class="elsevierStylePara elsevierViewall">Patient variables&#58; age&#44; gender&#44; body mass index &#40;BMI&#41;&#44; forced vital capacity &#40;FVC&#41;&#44; &#37;FEV<span class="elsevierStyleInf">1</span> and &#37;DLCO &#40;with optimized bronchodilator treatment&#44; if necessary&#41;&#59; comorbidities&#58; systemic hypertension &#40;HT&#41;&#44; ischemic cardiovascular disease&#44; diabetes mellitus &#40;DM&#41;&#44; chronic renal failure &#40;creatinine &#62;2<span class="elsevierStyleHsp" style=""></span>mg&#47;dl&#41;&#44; cardiac valve disease&#44; and&#47;or chronic arterial disease&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">-</span><p id="par0090" class="elsevierStylePara elsevierViewall">CPET variables&#58; patient&#8211;weight adjusted VO<span class="elsevierStyleInf">2peak</span> &#40;ml&#47;kg&#47;min&#41; in absolute values and percentage&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">-</span><p id="par0095" class="elsevierStylePara elsevierViewall">Pedometer variables&#58;<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">&#8226;</span><p id="par0100" class="elsevierStylePara elsevierViewall">Total number of steps per day or &#8220;total steps&#8221;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">&#8226;</span><p id="par0105" class="elsevierStylePara elsevierViewall">Number of aerobic steps per day or &#8220;aerobic steps&#8221;&#46; 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pneumonia according to Center for Disease Control&#47;National Healthcare Safety network criteria&#44;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> persistence of respiratory failure at discharge &#40;PO<span class="elsevierStyleInf">2</span>&#60;60<span class="elsevierStyleHsp" style=""></span>mmHg or PCO<span class="elsevierStyleInf">2</span>&#62;45<span class="elsevierStyleHsp" style=""></span>mmHg&#41;&#44; need for mechanical ventilation at any time after extubation in the operating room&#44; and occurrence of pulmonary thromboembolism&#46;</p></li><li class="elsevierStyleListItem" id="lsti0050"><span class="elsevierStyleLabel">&#8226;</span><p id="par0135" class="elsevierStylePara elsevierViewall">Cardiovascular<span class="elsevierStyleInf">&#58;</span> occurrence of stroke&#44; arrhythmia &#40;auricular fibrillation&#44; AF&#41;&#44; myocardial ischemia or acute myocardial infarction&#44; and heart failure&#46;</p></li><li class="elsevierStyleListItem" id="lsti0055"><span class="elsevierStyleLabel">&#8226;</span><p id="par0140" class="elsevierStylePara elsevierViewall">Technical<span class="elsevierStyleInf">&#58;</span> need for reoperation for massive hemothorax&#44; occurrence of prolonged air leak or bronchial fistula within the first week after resection&#46;</p></li></ul></p></li></ul></p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Statistical Method</span><p id="par0145" class="elsevierStylePara elsevierViewall">Descriptive and comparative statistical analysis of the study variables was performed&#46;</p><p id="par0150" class="elsevierStylePara elsevierViewall">After a correlation analysis between variables&#44; a linear regression model was designed&#44; where VO<span class="elsevierStyleInf">2max</span> was the dependent variable&#44; and the variables distance traveled&#44; &#37;DLCO and patient age were set as independent variables&#46; The outcome variable in this model was estimated VO<span class="elsevierStyleInf">2</span>&#46; Bootstrap technique with 1000 replications was performed to study collinearity and perform resampling&#44; in order to obtain a more robust standard error of the coefficients values&#46; Finally&#44; occurrence of postoperative morbidity and mortality and their relationship with VO<span class="elsevierStyleInf">2max</span> values&#44; both the measured values and the values estimated with the predictive model developed&#44; were analyzed using paired Student&#39;s <span class="elsevierStyleItalic">t</span>-test&#46; These analyses were performed using the statistical software Stata 10&#46;0&#46;</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Results</span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Final Study Population</span><p id="par0155" class="elsevierStylePara elsevierViewall">The final study population was 83 patients&#44; of whom 15 were women &#40;18&#46;1&#37;&#41;&#46; Population age was 64&#46;59&#177;9&#46;5 &#40;38&#8211;80 years&#41;&#46; Twenty patients &#40;24&#46;1&#37;&#41; were being treated for hypertension&#44; 18 &#40;21&#46;7&#37;&#41; met criteria for COPD and were receiving bronchodilators&#44; nine &#40;10&#46;8&#37;&#41; had DM&#44; six &#40;7&#46;2&#37;&#41; significant peripheral artery disease&#44; five &#40;6&#37;&#41; stable ischemic heart disease&#44; and one patient &#40;1&#46;2&#37;&#41; had renal failure not needing dialysis but strict medication adjustments&#46; None of the patients had associated valvular pathology&#46;</p><p id="par0160" class="elsevierStylePara elsevierViewall">Patients wore the pedometer for an average of 25 days &#40;range 5&#8211;41 days&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#44; showing a routine in the schedules and time periods they usually walked&#46; All patients performed the CPET according to protocol&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0165" class="elsevierStylePara elsevierViewall">Seventeen typical segmentectomies&#44; 61 lobectomies&#44; three pneumonectomies and two bilobectomies &#40;one right and one left&#41; were performed&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall">There was no mortality in this series&#46; In terms of morbidity&#44; 26 patients &#40;31&#46;3&#37;&#41; experienced a complication&#58; eight &#40;9&#46;6&#37;&#41; were respiratory&#44; one &#40;1&#46;2&#37;&#41; was a cardiac complication &#40;arrhythmia&#41;&#44; two patients had concomitant cardiac and respiratory complications&#44; and 15 patients experienced technical complications &#40;18&#46;1&#37;&#41;&#44; mainly prolonged air leak&#46;</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Preliminary Design Correlations and Linear Regression Model</span><p id="par0175" class="elsevierStylePara elsevierViewall">We analyzed the possible correlations between the recorded lung function variables and aerobic activity with VO<span class="elsevierStyleInf">2max</span> and VO<span class="elsevierStyleInf">2peak</span> data&#46;</p><p id="par0180" class="elsevierStylePara elsevierViewall">In this series&#44; VO<span class="elsevierStyleInf">2max</span> was significantly associated with age &#40;<span class="elsevierStyleItalic">r</span>&#61;&#8722;0&#46;451&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;000&#41;&#44; BMI &#40;<span class="elsevierStyleItalic">r</span>&#61;&#8722;0&#46;357&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;001&#41;&#44; DLCO &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;238&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;030&#41;&#44; number of total steps&#47;day &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;278&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;011&#41;&#44; and distance traveled&#47;day &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;315&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;004&#41;&#44; but not with &#37;FEV<span class="elsevierStyleInf">1</span> &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;182&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;1&#41;&#44; presence of COPD &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;088&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;431&#41;&#44; aerobic steps&#47;day &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;151&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;173&#41; or the total time of aerobic activity &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;117&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;294&#41;&#46;</p><p id="par0185" class="elsevierStylePara elsevierViewall">&#37;VO<span class="elsevierStyleInf">2</span> was significantly correlated only with &#37;FEV<span class="elsevierStyleInf">1</span> &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;303&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;006&#41;&#44; DLCO &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;390&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;000&#41;&#44; and the number of total steps&#47;day &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;244 steps&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;027&#41;&#46;</p><p id="par0190" class="elsevierStylePara elsevierViewall">After analyzing the correlations between variables&#44; the final linear regression model with VO<span class="elsevierStyleInf">2max</span> and &#37;VO<span class="elsevierStyleInf">2</span> as dependent variables included those variables with the highest correlation with the dependent variable and showing no collinearity between them&#46;</p><p id="par0195" class="elsevierStylePara elsevierViewall">Of the models developed for &#37;VO<span class="elsevierStyleInf">2</span> and for VO<span class="elsevierStyleInf">2max</span>&#44; the best fitting was related to the estimated VO<span class="elsevierStyleInf">2max</span> values &#40;dependent variable&#41; and included as independent variables age&#44; DLCO and distance traveled &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; Discriminant analysis for collinearity was performed and none of the variables were related to the others &#40;collinearity index &#60;15 in all variables&#41;&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Population Analysis According to Occurrence of Postoperative Cardiac Complications</span><p id="par0200" class="elsevierStylePara elsevierViewall">The two groups into which the population was divided were homogeneous &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0205" class="elsevierStylePara elsevierViewall">Both variables &#40;measured and estimated VO<span class="elsevierStyleInf">2</span>&#41; were normally distributed &#40;K&#8211;Smirnov <span class="elsevierStyleItalic">P</span>&#62;&#46;32&#41;&#46;</p><p id="par0210" class="elsevierStylePara elsevierViewall">As for prediction of complications&#44; mean estimated VO<span class="elsevierStyleInf">2</span> according to the model was significantly different between patients with&#47;without complication &#40;<span class="elsevierStyleItalic">P</span>&#61;&#46;037&#41;&#44; compared to measured VO<span class="elsevierStyleInf">2</span> values&#44; which did not distinguish these groups &#40;<span class="elsevierStyleItalic">P</span>&#61;&#46;42&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia></span></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Discussion</span><p id="par0215" class="elsevierStylePara elsevierViewall">The main objective of this study was to design a method for predicting the patient&#39;s VO<span class="elsevierStyleInf">2</span> from variables easily obtained in all patients&#44; and to analyze its predictive capacity for the occurrence of cardiac complications after lung resection&#46;</p><p id="par0220" class="elsevierStylePara elsevierViewall">The model developed had <span class="elsevierStyleItalic">R</span><span class="elsevierStyleSup">2</span>&#61;0&#46;94&#44; meaning a very good fit&#46; The three variables included are age&#44; &#37;DLCO and average distance traveled per day&#46; The inverse relationship between age and VO<span class="elsevierStyleInf">2</span> is well known&#44;<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> and inclusion of this variable into the analysis improves the result&#46; Both &#37;DLCO adjusted for the patient&#39;s hemoglobin and the average distance traveled per day in kilometers were already part of the previously published model&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> Inclusion of these two variables represented an innovation in the variables that had been used to estimate the VO<span class="elsevierStyleInf">2</span> in other studies&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#8211;14</span></a> In this analysis&#44; as in the previous one&#44;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> we chose to use the variable &#8220;distance&#8221; instead of &#8220;total steps&#8221; because its correlation index with VO<span class="elsevierStyleInf">2</span> was better &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;315 vs <span class="elsevierStyleItalic">r</span>&#61;0&#46;278&#41;&#46; There are two reasons for including &#37;DLCO&#58; first&#44; this parameter proved to be correlated with age &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;245&#41; and VO<span class="elsevierStyleInf">2max</span> &#40;<span class="elsevierStyleItalic">r</span>&#61;0&#46;238&#41; and&#44; second&#44; because &#37;DLCO is an accepted independent predictor of postoperative complications&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0225" class="elsevierStylePara elsevierViewall">The second objective was to analyze the predictive ability of these two variables of oxygen consumption<span class="elsevierStyleInf">&#58;</span> the measured one and the estimated one&#46; In our analysis&#44; predictive ability is significantly better in the estimated model&#46; Analyzing the performance of average values in the different groups&#44; two aspects draw attention &#40;<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#58; first&#44; the difference between groups with&#47;without complication is very small in both measurements&#59; second&#44; measured VO<span class="elsevierStyleInf">2</span> has very similar values&#44; and non-discriminating between the two groups because the ranges of measurements are very wide&#44; unlike estimated VO<span class="elsevierStyleInf">2</span> values whose ranges are much narrower and probably confer a predictive advantage&#46; A recent meta-analysis of Benzo et al&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> including 14 studies with 955 subjects found that patients with postoperative complications had significantly lower oxygen consumption than those who did not&#46; However&#44; average difference was only 3<span class="elsevierStyleHsp" style=""></span>ml&#47;kg&#47;min or 8&#46;95&#37; between the two groups&#46; The authors concluded from this analysis that patients with consumption over 20<span class="elsevierStyleHsp" style=""></span>ml&#47;kg&#47;min had a very low complication rate&#46; This did not mean that complications would not occur&#44; only that in case of occurrence&#44; they had a better chance of survival&#46; Interestingly&#44; the range of values of measured VO<span class="elsevierStyleInf">2</span> in patients who experienced complications in our study was higher than in those who did not &#40;17&#46;7&#8211;19&#46;8 vs 16&#46;2&#8211;21&#46;4&#41; and mean values were similar &#40;18&#46;75 vs 18&#46;85&#59; <span class="elsevierStyleItalic">P</span>&#61;&#46;42&#41;&#46; However&#44; the ranges of estimated consumption values are less wide &#40;18&#46;3&#8211;19&#46;4 vs 16&#46;1&#8211;18&#46;5&#41; and means were significantly different &#40;18&#46;87&#8211;17&#46;35&#59; <span class="elsevierStyleItalic">P</span>&#61;&#46;037&#41;&#46; In the meta-analysis&#44;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> the lower threshold associated with a high complication rate is 16<span class="elsevierStyleHsp" style=""></span>ml&#47;kg&#47;min&#44; and this is 17&#46;35<span class="elsevierStyleHsp" style=""></span>ml&#47;kg&#47;min in our patients&#44; slightly higher but within the range of values published in studies analyzed&#46;</p><p id="par0230" class="elsevierStylePara elsevierViewall">Although the comparison of the characteristics of these two subpopulations of patients&#8212;those who experience complications and those who do not&#8212;yields two statistically similar groups&#44; patients who experience more complications have older mean age &#40;63&#46;8&#8211;69&#46;2 years&#41; and lower &#37;DLCO &#40;78&#46;2&#8211;66&#46;7&#41;&#46; These data are consistent with published results regarding the characteristics of patients who experience complications in other series&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p><p id="par0235" class="elsevierStylePara elsevierViewall">Current clinical guidelines<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> for the evaluation of patients eligible for lung resection recommend a final decision based on the CPET results&#44; i&#46;e&#46;&#44; according to the VO<span class="elsevierStyleInf">2max</span> reached expressed as absolute values or percentage &#40;&#37;VO<span class="elsevierStyleInf">2</span>&#41;&#46;</p><p id="par0240" class="elsevierStylePara elsevierViewall">The values on which VO<span class="elsevierStyleInf">2max</span> depends include age&#44;<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> gender&#44;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> previous respiratory disease&#44;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> genetic characteristics&#44;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> and the training degree of the patient&#44;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> among other factors&#46; The training degree is in turn is strongly linked to daily physical activity&#44;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> his&#47;her health status&#44; and mortality&#44;<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">21&#44;22</span></a> and may be useful for predicting postoperative adverse events&#46; In this regard&#44; our group has sought an alternative to estimate VO<span class="elsevierStyleInf">2max</span> value by studying performance at an easy exercise&#46;</p><p id="par0245" class="elsevierStylePara elsevierViewall">Recent studies<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> confirm the existence of numerous shortcomings in the implementation of the European functional assessment algorithm published in 2009&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> The major difficulty is conducting high-technology stress testing&#44; since only 84&#37; of operated patients underwent this for various reasons&#46; The same guidelines recognize that the standardized symptom-limited stair climbing test can be an alternative to performing the CPET&#44; helping reduce the number of patients who would require the latter&#46; However&#44; this test has been criticized for its poor standardization and difficulty for performance in a safe place for the patient&#46; In addition&#44; many patients have joint and insecurity problems when climbing stairs&#44; so results can sometimes be suboptimal&#44; as occurs with the CPET&#44; in which some unsatisfactory results may be attributable to the harsh conditions required for its performance&#46;</p><p id="par0250" class="elsevierStylePara elsevierViewall">Patient limitations are one of the root causes for not being able to perform the CPET&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> Cardiovascular contraindications and joint limitations are especially relevant in an increasingly older population&#44; with more associated comorbidity&#46; Overcoming this limitation is difficult&#44; but possibly the solution lies in the development of new simple and safe strategies to measure the exercise capacity of a given patient&#46; This is the case of measurements based on walked distance or the quality of daily exercise that the patient performs in everyday life&#46;</p><p id="par0255" class="elsevierStylePara elsevierViewall">It is not the intention of this study to question the predictive value of the CPET in surgical risk evaluation&#46; At present&#44; the measurement of daily physical activity is not considered a substitute for the formal CPET&#44; but further studies with larger series and patients more functionally compromised might allow may support this as a valid&#44; cost-saving and effective screening test for those patients in which a formal CPET cannot be conducted due to contraindication or limitation&#44; or also as an alternative when CPET is not available in the center&#46;</p><p id="par0260" class="elsevierStylePara elsevierViewall">Our study has some important limitations&#46; On the one hand&#44; the limitation derived from its sample size&#44; which limits the possibilities for statistical study&#46; This reason prevented a division of the sample into two groups&#58; one to create the mathematical model and a second group to assess validity&#46; Analysis of larger series in the future will enable testing of the reliability of the conclusions drawn in this study&#46; This aspect also influences the number of cardiac complications included in the study&#44; experienced by only 11 patients&#44; which also limits the ability to extrapolate the results&#46; Another important limitation of this study is a possible observation bias&#46; The data collected were generated by the patient during everyday life outside the hospital&#46; Therefore&#44; it is not possible to be absolutely certain that this person was wearing the pedometer&#46; However&#44; in order to reduce this bias&#44; special emphasis was made to each patient during consultation on the importance of not letting anyone else from his&#47;her environment use the pedometer&#46; Lastly&#44; the findings may be affected by case-selection bias&#44; since it was not possible to obtain data from patients who were not considered candidates for surgery&#44; thereby not eligible for this study&#44; due to the selection criteria of the physicians who refer patients to our unit&#46;</p><p id="par0265" class="elsevierStylePara elsevierViewall">In conclusion&#44; the VO<span class="elsevierStyleInf">2</span> value of a given patient can be reliably estimated from age&#44; &#37;DLCO and average walking distance per day&#44; measured with a pedometer&#46; In this series&#44; estimated VO<span class="elsevierStyleInf">2max</span> was more predictive of the occurrence of cardiac complications than VO<span class="elsevierStyleInf">2max</span> measured in a standard exercise test&#46; Our next objective will be the validation of this model in a larger series and with a broader patient profile&#46;</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Conflicts of Interest</span><p id="par0270" class="elsevierStylePara elsevierViewall">The authors declare no conflict of interest that might cause a bias in this study&#46;</p></span></span>"
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      "en" => array:2 [
        "titulo" => "Abstract"
        "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0010">Objectives</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The aims of this study were to design a best fit linear regression model to estimate VO<span class="elsevierStyleInf">2max</span> &#40;estimated VO<span class="elsevierStyleInf">2</span>&#41; and to compare the ability of VO<span class="elsevierStyleInf">2</span> values &#40;measured and estimated&#41; predicting cardiorespiratory complications in a series of patients undergoing lung resection for lung cancer&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0015">Method</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">This was a prospective&#44; observational study performed in 83 patients&#46; Variables analyzed were&#58; demographic characteristics&#44; comorbidity&#44; body mass index &#40;BMI&#41;&#44; FEV1&#37;&#44; FVC&#37;&#44; diffusion capacity &#40;DLCO&#37;&#41;&#44; mean daily distance walked in kilometers&#44; VO<span class="elsevierStyleInf">2max</span> measured by cardio-pulmonary exercise test &#40;CPET&#41; and postoperative complications&#46; Descriptive and comparative statistical analysis of the variables was performed using the Mann&#8211;Whitney test for categorical variables and the Student&#39;s <span class="elsevierStyleItalic">t</span>-test for continuous variables&#46; A new linear regression model was designed&#44; where the dependent variable &#40;measured VO<span class="elsevierStyleInf">2max</span>&#41; was estimated by the distance&#44; DLCO&#37; and age&#44; resulting in the estimated VO<span class="elsevierStyleInf">2</span>&#46; The predictive power of the measured and estimated consumption was analyzed using the Student&#39;s <span class="elsevierStyleItalic">t</span>-test&#44; grouping by the occurrence or absence of cardiorespiratory complications&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Both groups were homogeneous for age&#44; sex&#44; BMI&#44; FEV1&#37;&#44; DLCO&#37;&#44; comorbidity&#44; type of resection performed and mean distance walked per day&#46; Estimated VO<span class="elsevierStyleInf">2</span> and measured VO<span class="elsevierStyleInf">2</span> were normally distributed &#40;K&#8211;Smirnov test&#44; <span class="elsevierStyleItalic">P</span>&#62;&#46;32&#41;&#46; VO<span class="elsevierStyleInf">2</span> means estimated by the model &#40;age&#44; DLCO&#37; and mean distance walked per day&#41; were significantly different between patients with and without complications &#40;Student&#39;s <span class="elsevierStyleItalic">t</span>-test&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;037&#41; compared with measured VO<span class="elsevierStyleInf">2</span> values&#44; which did not differentiate groups &#40;Student&#39;s <span class="elsevierStyleItalic">t</span>-test&#44; <span class="elsevierStyleItalic">P</span>&#61;&#46;42&#41;&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The VO<span class="elsevierStyleInf">2max</span> estimated by the model is more predictive in this case series than the VO<span class="elsevierStyleInf">2max</span> measured during a standard exercise test&#46;</p>"
      ]
      "es" => array:2 [
        "titulo" => "Resumen"
        "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0035">Objetivos</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Los objetivos de este trabajo son redise&#241;ar un modelo de regresi&#243;n lineal para estimar el valor de VO<span class="elsevierStyleInf">2max</span> &#40;VO<span class="elsevierStyleInf">2</span> estimado&#41; ya publicado y comparar la capacidad predictiva de los valores VO<span class="elsevierStyleInf">2</span> medido y VO<span class="elsevierStyleInf">2</span> estimado en la aparici&#243;n de complicaciones cardiorrespiratorias en una serie de pacientes sometidos a resecci&#243;n pulmonar por c&#225;ncer de pulm&#243;n&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0040">M&#233;todo</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Estudio prospectivo y observacional en 83 pacientes&#46; Variables analizadas&#58; demogr&#225;ficas&#44; comorbilidad&#44; IMC&#44; FEV1&#37;&#44; FVC&#37;&#44; DLCO&#37;&#44; distancia recorrida media diaria &#40;km&#41;&#44; VO<span class="elsevierStyleInf">2max</span> medido en el test de ejercicio cardiopulmonar &#40;CPET&#41; y complicaci&#243;n postoperatoria&#46; Estad&#237;stica descriptiva y comparativa de variables usando Mann-Whitney test para categ&#243;ricas y t de Student para continuas normales&#46; Se dise&#241;a un modelo de regresi&#243;n lineal donde VO<span class="elsevierStyleInf">2max</span>&#44; la variable dependiente&#44; se estima desde la distancia recorrida&#44; DLCO&#37; y edad del paciente y su resultado es la VO<span class="elsevierStyleInf">2</span> estimada&#46; Se compara la capacidad predictiva de los VO<span class="elsevierStyleInf">2max</span> medido y estimado mediante t de Student&#44; agrupando por ocurrencia o no de complicaciones cardiorrespiratorias&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Los 2 grupos son homog&#233;neos en edad&#44; distribuci&#243;n de sexos&#44; IMC&#44; FEV1&#37;&#44; DLCO&#37;&#44; comorbilidad&#44; cirug&#237;a realizada y distancia media recorrida&#47;d&#237;a&#46; VO<span class="elsevierStyleInf">2</span> medida y VO<span class="elsevierStyleInf">2</span> estimada tienen distribuci&#243;n normal &#40;K-Smirnov p<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>0&#44;32&#41;&#46; En la predicci&#243;n de complicaciones&#44; las medias del VO<span class="elsevierStyleInf">2</span> estimado a partir del modelo son significativamente diferentes entre los pacientes con&#47;sin complicaci&#243;n &#40;t de Student p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#44;037&#41;&#59; frente a los valores de VO<span class="elsevierStyleInf">2</span> medido que no distinguen grupos &#40;t de Student p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#44;42&#41;&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">El VO<span class="elsevierStyleInf">2max</span> estimado por el modelo es m&#225;s predictivo en esta serie de casos que el VO<span class="elsevierStyleInf">2max</span> medido en una CPET&#46;</p>"
      ]
    ]
    "NotaPie" => array:1 [
      0 => array:2 [
        "etiqueta" => "&#9734;"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as&#58; Vargas Fajardo MdC&#44; Novoa Valent&#237;n NM&#44; Jim&#233;nez L&#243;pez MF&#44; Ramos Gonzalez J&#44; Varela Sim&#243; G&#46; Un m&#233;todo alternativo para la predicci&#243;n del riesgo de complicaciones postoperatorias en la resecci&#243;n pulmonar&#46; 2014&#59;50&#58;87&#8211;92&#46;</p>"
      ]
    ]
    "multimedia" => array:5 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Fig&#46; 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Differences between VO<span class="elsevierStyleInf">2max</span> and VO<span class="elsevierStyleInf">2</span> mean values&#44; estimated according to the occurrence of postoperative cardiac complications&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
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                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Variable&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Minimum&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Standard Deviation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="5" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Pedometer activity data</span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Total steps&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">821&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">20<span class="elsevierStyleHsp" style=""></span>471&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9508&#46;83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4665&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Aerobic steps&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10<span class="elsevierStyleHsp" style=""></span>904&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3688&#46;47&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2989&#46;35&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Time &#40;min&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">108&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">35&#46;66&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;78&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Distance &#40;km&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;37&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;19&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="5" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="5" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Measured VO</span><span class="elsevierStyleInf"><span class="elsevierStyleItalic">2</span></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>VO<span class="elsevierStyleInf">2</span> &#40;ml&#47;kg&#47;min&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">31&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">18&#46;79&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>VO<span class="elsevierStyleInf">2</span> &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">48&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">116&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">78&#46;54&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;39&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
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          "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Characteristics of Pedometer-recorded Activity and CPET-measured Oxygen Consumption by Study Population&#46;</p>"
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                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Wald test&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">Significance&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">&#946;</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">95&#37; CI of <span class="elsevierStyleItalic">B</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;49&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;000&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;398&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;066&#8211;0&#46;170&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">DLCO&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;007&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;414&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;054&#8211;0&#46;148&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Distance&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;19&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;002&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;186&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;204&#8211;0&#46;286&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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                0 => "xTab466430.png"
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Characteristics of the Best Fit Linear Regression Model&#46;</p>"
        ]
      ]
      3 => array:7 [
        "identificador" => "tbl0015"
        "etiqueta" => "Table 3"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:1 [
          "tablatextoimagen" => array:2 [
            0 => array:2 [
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                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Variable&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Patients Without CR Complications &#40;<span class="elsevierStyleItalic">n</span>&#61;72&#41;<span class="elsevierStyleItalic">n</span> &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Patients With CR Complications &#40;<span class="elsevierStyleItalic">n</span>&#61;11&#41;<span class="elsevierStyleItalic">n</span> &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Gender&#58; male&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11 &#40;100&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#46;201&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hypertension&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">18 &#40;25&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2 &#40;18&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">COPD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">13 &#40;18&#46;05&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5 &#40;45&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#46;055&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">DM&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8 &#40;11&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1 &#40;9&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">IHD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5 &#40;6&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial disease&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4 &#40;5&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2 &#40;18&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#46;178&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">RF&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1 &#40;1&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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                0 => "xTab466433.png"
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            1 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Mean &#40;DE&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Mean &#40;DE&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">P</span>-Value&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63&#46;8 &#40;9&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">69&#46;2 &#40;8&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#46;08&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">IMC&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">26&#46;7 &#40;4&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&#46;9 &#40;4&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">&#46;78&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#37;FEV<span class="elsevierStyleInf">1</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">85&#46;1 &#40;20&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">77&#46;5 &#40;24&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">&#46;26&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#37;FVC&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">96&#46;4 &#40;19&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">100&#46;2 &#40;19&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#46;53&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#37;DLCO&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">78&#46;2 &#40;20&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">66&#46;78 &#40;20&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#46;08&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Total footsteps&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9511&#46;2 &#40;4701&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9493&#46;2 &#40;4641&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Aerobic footsteps&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">3750&#46;6 &#40;2921&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">3281&#46;5 &#40;3533&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">Time&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">36&#46;1 &#40;26&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Distance&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">5&#46;9 &#40;3&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">Variable&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;7&#8211;19&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">&#46;42&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>With CR complication&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">11&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">18&#46;85&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">3&#46;83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;2&#8211;21&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="6" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="6" align="char" valign="\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">72&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">18&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#46;28&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">18&#46;3&#8211;19&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">&#46;037&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>With CR complication&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">11&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">17&#46;35&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">16&#46;1&#8211;18&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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          "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Numerical Values of Mean Oxygen Consumption by Groups According to Occurrence of Cardiopulmonary Complications &#40;CR&#41;&#46;</p>"
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                    0 => array:2 [
                      "titulo" => "European Respiratory Society and European Society of Thoracic Surgeons joint task force on fitness for radical therapy&#46; ERS&#47;ESTS clinical guidelines on fitness for radical therapy in lung cancer patients &#40;surgery and chemo-radiotherapy&#41;"
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                            0 => "A&#46; Brunelli"
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                            2 => "C&#46;T&#46; Bolliger"
                            3 => "G&#46; Rocco"
                            4 => "J&#46;P&#46; Sculier"
                            5 => "G&#46; Varela"
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                        "tituloSerie" => "Eur Respir J"
                        "fecha" => "2009"
                        "volumen" => "34"
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                        "paginaFinal" => "41"
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                            "url" => "https://www.ncbi.nlm.nih.gov/pubmed/19567600"
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                0 => array:2 [
                  "contribucion" => array:1 [
                    0 => array:2 [
                      "titulo" => "The use of scoring systems in selecting patients for lung cancer resection&#58; work-up bias come full-circle"
                      "autores" => array:1 [
                        0 => array:2 [
                          "etal" => false
                          "autores" => array:2 [
                            0 => "M&#46; Utley"
                            1 => "T&#46; Treasure"
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                  ]
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                    0 => array:2 [
                      "doi" => "10.1016/j.thorsurg.2007.10.005"
                      "Revista" => array:6 [
                        "tituloSerie" => "Thorac Surg Clin"
                        "fecha" => "2008"
                        "volumen" => "18"
                        "paginaInicial" => "107"
                        "paginaFinal" => "113"
                        "link" => array:1 [
                          0 => array:2 [
                            "url" => "https://www.ncbi.nlm.nih.gov/pubmed/18402206"
                            "web" => "Medline"
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                  "contribucion" => array:1 [
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                      "titulo" => "The evolving role of exercise testing prior to lung resection"
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                          "autores" => array:1 [
                            0 => "G&#46;N&#46; Olsen"
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                    0 => array:1 [
                      "Revista" => array:6 [
                        "tituloSerie" => "Chest"
                        "fecha" => "1989"
                        "volumen" => "95"
                        "paginaInicial" => "218"
                        "paginaFinal" => "225"
                        "link" => array:1 [
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                            "url" => "https://www.ncbi.nlm.nih.gov/pubmed/2642409"
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                      "titulo" => "Performance at symptom-limited stair-climbing test is associated with increased cardiopulmonary complications&#44; mortality&#44; and costs after major lung resection"
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Article information

ISSN: 15792129
Original language: English

DOI:10.1016/j.arbr.2014.02.004

The statistics are updated each day

Year/Month	Html	Pdf	Total

2024 November	3	2	5
2024 October	39	20	59
2024 September	39	19	58
2024 August	52	39	91
2024 July	50	22	72
2024 June	55	24	79
2024 May	79	31	110
2024 April	45	19	64
2024 March	41	20	61
2024 February	45	16	61
2023 March	4	8	12
2023 February	31	31	62
2023 January	25	30	55
2022 December	33	32	65
2022 November	36	24	60
2022 October	34	40	74
2022 September	33	21	54
2022 August	32	31	63
2022 July	37	44	81
2022 June	35	29	64
2022 May	31	27	58
2022 April	30	31	61
2022 March	35	40	75
2022 February	30	16	46
2022 January	31	26	57
2021 December	36	35	71
2021 November	53	54	107
2021 October	27	49	76
2021 September	29	49	78
2021 August	22	34	56
2021 July	31	34	65
2021 June	42	31	73
2021 May	42	33	75
2021 April	92	78	170
2021 March	63	13	76
2021 February	49	21	70
2021 January	38	7	45
2020 December	28	23	51
2020 November	33	15	48
2020 October	34	11	45
2020 September	80	15	95
2020 August	38	17	55
2020 July	31	21	52
2020 June	38	5	43
2020 May	28	12	40
2020 April	40	20	60
2020 March	41	14	55
2020 February	38	22	60
2020 January	39	14	53
2019 December	47	14	61
2019 November	49	9	58
2019 October	22	4	26
2019 September	43	19	62
2019 August	24	12	36
2019 July	43	18	61
2019 June	40	15	55
2019 May	44	8	52
2019 April	28	12	40
2019 March	62	18	80
2019 February	63	28	91
2019 January	55	25	80
2018 December	46	21	67
2018 November	99	16	115
2018 October	194	24	218
2018 September	39	13	52
2018 May	18	0	18
2018 April	27	10	37
2018 March	26	5	31
2018 February	35	6	41
2018 January	29	5	34
2017 December	26	7	33
2017 November	23	5	28
2017 October	34	6	40
2017 September	44	9	53
2017 August	54	13	67
2017 July	50	9	59
2017 June	51	20	71
2017 May	48	5	53
2017 April	30	25	55
2017 March	28	5	33
2017 February	19	6	25
2017 January	10	3	13
2016 December	31	6	37
2016 November	42	9	51
2016 October	42	18	60
2016 September	63	10	73
2016 August	72	13	85
2016 July	35	9	44
2016 March	1	0	1
2016 February	1	0	1
2015 December	3	0	3
2015 October	44	1	45
2015 September	49	9	58
2015 August	47	12	59
2015 July	46	6	52
2015 June	30	3	33
2015 May	42	15	57
2015 April	37	11	48
2015 March	51	4	55
2015 February	36	6	42
2015 January	40	4	44
2014 December	24	5	29
2014 November	31	10	41
2014 October	34	15	49
2014 September	27	13	40
2014 August	35	15	50
2014 July	38	12	50
2014 June	42	18	60
2014 May	1	0	1
2014 March	0	1	1

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